Academy & Industry Research Collaboration Center
Scientific computation requires more and more performance in its algorithms. New massively parallel architectures suit well to these algorithms. They are known for offering high performance and power efficiency. Unfortunately, as parallel programming for these architectures requires a complex distribution of tasks and data, developers find difficult to implement their applications effectively. Although approaches based on source-to-source intends to provide a low learning curve for parallel programming and take advantage of architecture features to create optimized applications, programming remains difficult for neophytes.